Search results for "Co-occurrence matrix"

showing 3 items of 3 documents

Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongoli…

2020

11 pages; International audience; The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive…

Archeology010504 meteorology & atmospheric sciences[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and PrehistoryComputer scienceMaterials Science (miscellaneous)Topographic position index[SDV]Life Sciences [q-bio]ConservationMachine learningcomputer.software_genre01 natural sciences[SHS]Humanities and Social SciencesNaive Bayes classifierVector graphicsPixel classification[SCCO]Cognitive sciencePixel classification Grey level co-occurrence matrix RGB colour space Texture Topographic position index Photogrammetry Burial complex planigraphy Mongolia Bronze age Iron age0601 history and archaeologyTextureSpectroscopyRGB colour space0105 earth and related environmental sciencesBronze age060102 archaeologyArtificial neural networkbusiness.industryIron ageCentroidGrey level co-occurrence matrix06 humanities and the artscomputer.file_formatMongoliaArchaeologyRandom forestSupport vector machinePhotogrammetryChemistry (miscellaneous)Photogrammetry[SDE]Environmental SciencesBurial complex planigraphyArtificial intelligenceRaster graphicsbusinessGeneral Economics Econometrics and Financecomputer
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Dissimilarity Application for Medical Imaging Classification

2005

In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) die training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 col…

Breast cancerDissimilarityComputer assisted diagnosiComputer aided diagnosimammographyCo-occurrence matrixMedical image processingimage segmentationNeural network
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New methods for analysing colour texture based on the Karhunen–Loeve transform and quantification

2004

In this article, we offer an original study on the analysis of the texture of colour images based on Local Linear Transforms (LLT). Our colour approach is based on the separability of the data which reduces the number of texture parameters. We also propose the extension of Run Lengths (RL) and Co-occurrence Matrixes (CM) to colour images. In this respect, two different ways were explored (data merging and quantification). We finally present a comparative study showing the efficiency of the first method (LLT) as well as the complementary nature of the other methods (RL, CM).

Karhunen–Loève theoremLocal linearbusiness.industryExtension (predicate logic)Texture (geology)Co-occurrence matrixArtificial IntelligenceSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareMathematicsPattern Recognition
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